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• W2320007293 abstract "The literature proposes numerous so-called pseudo-R2 measures for evaluating “goodness of fit” in regression models with categorical dependent variables. Unlike ordinary least square-R2, log-likelihood-based pseudo-R2s do not represent the proportion of explained variance but rather the improvement in model likelihood over a null model. The multitude of available pseudo-R2 measures and the absence of benchmarks often lead to confusing interpretations and unclear reporting. Drawing on a meta-analysis of 274 published logistic regression models as well as simulated data, this study investigates fundamental differences of distinct pseudo-R2 measures, focusing on their dependence on basic study design characteristics. Results indicate that almost all pseudo-R2s are influenced to some extent by sample size, number of predictor variables, and number of categories of the dependent variable and its distribution asymmetry. Hence, an interpretation by goodness-of-fit benchmark values must explicitly consider these characteristics. The authors derive a set of goodness-of-fit benchmark values with respect to ranges of sample size and distribution of observations for this measure. This study raises awareness of fundamental differences in characteristics of pseudo-R2s and the need for greater precision in reporting these measures." @default.
• W2320007293 created "2016-06-24" @default.
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• W2320007293 date "2016-03-18" @default.
• W2320007293 modified "2023-10-12" @default.
• W2320007293 title "Log-likelihood-based Pseudo-<i>R</i>²in Logistic Regression" @default.
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• W2320007293 doi "https://doi.org/10.1177/0049124116638107" @default.
• W2320007293 hasPublicationYear "2016" @default.
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